This course traces the fascinating evolution of artificial intelligence, guiding you from the biological inspiration of the human brain to the earliest mathematical models of artificial neurons. You will master the core mechanics of how machines learn by diving deep into perceptrons, optimization, and backpropagation before scaling up to advanced architectures like Convolutional and Recurrent Neural Networks. By the end of the journey, you will understand how deep learning powers modern breakthroughs—from object detection and machine translation to generative art—and you will seamlessly bridge rigorous mathematical theory with hands-on practice by coding these foundational algorithms from scratch using modern frameworks like PyTorch.